Using hash signatures of dom objects to identify website similarity

ABSTRACT

Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit of andpriority to U.S. patent application Ser. No. 14/938,814, filed Nov. 11,2015 (now allowed), entitled “USING HASH SIGNATURES OF DOM OBJECTS TOIDENTIFY WEB SITE SIMILARITY,” which claims the benefit of priority toU.S. Provisional Application No. 62/219,624 filed Sep. 16, 2015, both ofwhich are hereby incorporated by reference in their entirety for allpurposes.

This application is related to U.S. Non-provisional application Ser. No.14/938,802, titled “IDENTIFYING PHISHING WEBSITES USING DOMCHARACTERISTICS,” filed on Nov. 11, 2015, which claims the benefit ofpriority to U.S. Provisional Application No. 62/219,623 filed Sep. 16,2015. Both of the above-referenced applications are hereby incorproatedby reference in their entirety for all purposes.

BACKGROUND

As the use of the Internet and the amount of information available onthe Internet has expanded, the ability to track and monitor informationavailable over the Internet related to a particular subject orassociated with a particular entity has been negatively impacted. Assuch, it can be difficult for entities with a presence on the Internetto provide a consistent experience and information to the public. Thevast amount of information present on the Internet makes monitoringwebsites nearly impossible as it is difficult to quickly and efficientlycompare the large amount of information contained within the largenumber of websites that may be associated with an entity. Accordingly,malicious third parties may hide malicious code from an entity's webdomain without such entities knowing that any changes have occurred orthat such domains have been taken over by malicious code. As such, it isdifficult to ensure that malicious third parties are not altering,misappropriating, and/or using their information, intellectual property,and goodwill without their knowledge.

Accordingly, there is a need for systems that are capable ofdiscovering, cataloging, and monitoring websites on behalf of entitiesto determine changes to websites and identifying malicious activityassociated with those websites.

Embodiments of the present invention solve these and other problemsindividually and collectively.

BRIEF SUMMARY

Embodiments are directed to using a hash signature of a rendered DOMobject of a website to find similar content and behavior on otherwebsites. Embodiments break a DOM into a large number of data portions(i.e., “shingles”), apply a hashing algorithm to the shingles, select apredetermined number of hashes from the hashed shingles according to aselection criteria to create a hash signature, and compare the hashsignature to that of a reference page to determine similarity of websiteDOM object content. Embodiments can be used to identify phishingwebsites, defaced websites, spam websites, significant changes in thecontent of a webpage, copyright infringement, and any other suitablepurposes related to the similarity between website DOM object content.

One embodiment of the present invention is directed to a method fordetermining a similarity between two websites. The method comprises acomputer system receiving website information from a web servercorresponding to a website, rendering a document object model (DOM)object of the website using the website information, separating contentwithin the DOM object into a plurality of data portions, each of theplurality of data portions having a fixed length, and generating a hashsignature of the DOM object by applying a hashing function to each ofthe plurality of data portions. The method further comprises thecomputer comparing the hash signature of the DOM object to a known hashsignature of a DOM object associated with a known website having a firstclassification. The comparison includes comparing each of the pluralityof hashed data portions to a plurality of known hashed data portions ofthe known hash signature. The method further comprises calculating asimilarity measurement between the hash signature of the DOM object andthe known hash signature of the DOM object associated with the knownwebsite, comparing the similarity measurement to a threshold, anddetermining the website has the first classification based on thesimilarity measurement exceeding the threshold.

Other embodiments are directed to systems, portable consumer devices,and computer readable media associated with methods described herein.

A better understanding of the nature and advantages of the presentinvention may be gained with reference to the following detaileddescription and the accompanying drawings.

Reference to the remaining portions of the specification, including thedrawings and claims, will realize other features and advantages of thepresent invention. Further features and advantages of the presentinvention, as well as the structure and operation of various embodimentsof the present invention, are described in detail below with respect tothe accompanying drawings. In the drawings, like reference numbers canindicate identical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a website crawling and discovery system, according to oneembodiment of the present invention.

FIG. 2 shows a data analysis system including a computing device that isconfigured to determine the similarity of a website to previously storedand classified web sites, according to one embodiment of the presentinvention.

FIG. 3 shows an exemplary method of generating a hash signature of a DOMobject of a website and classifying the hash signature as confirmed ordismissed for a classification, according to one embodiment of thepresent invention.

FIG. 4 shows an exemplary DOM object and a corresponding exemplary HTMLcode body response, according to one embodiment of the presentinvention.

FIG. 5 shows the changes to the DOM object as some functions containedwith the website information are executed, according to one embodimentof the present invention.

FIG. 6 shows an exemplary method for separating a document into fixedlength data portions, according to one embodiment of the presentinvention.

FIG. 7 shows an exemplary view of a hashing algorithm, according to oneembodiment of the present invention.

FIG. 8 shows an exemplary method of applying a predetermined number ofhashing algorithms to fixed length data portions and selecting hashvalues based on an exemplary selection policy, according to oneembodiment of the present invention.

FIG. 9 shows an exemplary method of using locality sensitive hashing(LSH) to further group hash signatures by similarity, according to oneembodiment of the present invention.

FIG. 10 shows an exemplary classified website hash signature databasethat includes hash signatures of websites that have been classified insome exemplary classifications, according to one embodiment of thepresent invention.

FIG. 11 shows an exemplary method of generating a hash signature for aDOM object of a website and comparing to known classified hashsignatures of other websites to classify the website, according to oneembodiment of the present invention.

FIG. 12 shows an exemplary method of classifying a website based on adatabase of classified known hash signatures based on a similaritymeasurement, according to one embodiment of the present invention.

FIG. 13 shows an exemplary result of a weighting calculation fordetermining the similarity measurement and for performing aclassification of a website, according to one embodiment of the presentinvention.

FIG. 14 shows an exemplary computer system.

TERMS

A “Document Object Model object” or “DOM object” is a platform- andlanguage-neutral interface that allows programs and scripts todynamically access and update the content, structure and style ofdocuments. The documents may include any data that can be exchanged andstored by computer environments. For example, a document may includereceived HTML code for a webpage. The DOM object may define the logicalstructure of documents and the way a document is accessed andmanipulated through a tree structure, called a DOM tree. The documentcan be further processed and the results of that processing can beincorporated back into the presented page to provide an updated DOMobject. The DOM object allows client-side applications to dynamicallyaccess, interact, and update information received from one or moreremote server computers associated with website information receivedfrom a web server computer.

“Web site information” may include any relevant information associatedwith a host website. For example, website information may include a URLfor the website, the HTML code received once contacting the web server,instructions for contacting other remote server computers for content,JavaScript functionality for loading executable information within thewebsite, meta data associated with the HTML code, and any otherinformation that may be received from a web server for rendering awebpage associated with a website.

“Shingling” may include any process of breaking information intoconsistent portions of data, each of the data portions being apredetermined length. For example, shingling may include picking awindow size and sliding the chosen window over content within a documentor object such that it produces contiguous subsequences of the textunder consideration. For instance, separating content within the DOMobject may include shingling the text, HTML headers, labels, and anyother information included within a rendered DOM object into a pluralityof data portions of equal and fixed length. For example, each of thedata portions may be ten characters long.

A “hashing function” may include any function that can be used to mapdigital data of arbitrary size to digital data of fixed size. The valuesreturned by a hash function may be called hash values, hash codes, hashsums, or simply hashes. In some embodiments, applying a hashing functionmay include applying a predetermined number of permutations of a hashingfunction to a set of data to create a predetermined number of hashes foreach piece of data within a set of data. In some embodiments, aselection policy may be applied to the predetermined number of hashes toselect a single value for each distinct piece of data within the set ofdata. For example, applying a MinHash hashing function may includecomputing hashes of the text shingles N number of times, and for allsequence of hash values, choosing the minimum value for each sequenceoffset. Thus, embodiments sample a set of hashes and reduces the amountof data required for a signature. The set of computed minimum hashvalues in the hash signature can then be used to estimate JaccardSimilarity. However, doing a pairwise comparison to determine eachdocument similar to each other document would typically require orderO(n²) operations.

A “hash signature” may include a characterization of the content withina document to identify a distinctive pattern, product, or characteristicby which someone or something can be identified using one or more hashfunctions. The hash signature may include a predetermined number of hashvalues that are a sample of the content within a document. A hashsignature allows the content within a document to be limited to apredetermined number of samples to limit the amount of information to becompared between documents.

Additionally, in some embodiments, applying a hashing function mayinclude “Locality Sensitive Hashing” (LSH) which computes a hash ofgroups of hashed values. For example, a grouping of MinHash valueshashed together may be referred to as a band. The collection of hashedbands computed during LSH samples the MinHash values and further reducesthe amount of data required to determine similar documents. Thus, theLSH processing samples the MinHash signatures to further compress adocument signature. Documents can be compared by determining if a subsetof their LSH buckets match. Because the values are hashed into buckets,the system may obtain candidate pairs for matching similarity bydetermining if they share the same bucket. Comparing LSH values andtheir offsets between two documents gives candidate pairs. This can bedone in order O(n) time. If a match, a candidate pair is found. Thedocuments may then be compared using a similarity measurement betweenthe two documents to determine a similarity between the documents thatshare the same bucket.

A “similarity measurement” may include a result of applying a functionthat quantifies the similarity between two objects. A variety ofdifferent functions may be applied to determine a similarity measurementbetween two or more documents. For example, a Jaccard similaritymeasurement includes a quantified similarity between two sets ofinformation by calculating the magnitude of the set intersection (i.e.,the set of shared elements between two sets of elements) divided by theset union (i.e., the set of distinct elements between two sets ofelements). An estimation of the Jaccard similarity may be provided bycalculating the number of matching hash values divided by the totalnumber of hash values.

A “classification” may include a category into which information isassigned and/or associated. Classifications may include any suitabletype of information and/or associations with an activity. For example, aclassification may include a characteristic of the underlying classifiedobject and/or may be associated with an action or functionalityassociated with the classified object. In some embodiments, aclassification may include examples of both confirmed objects associatedwith that activity or characteristic as well dismissed objects that areconfirmed as not being associated with the activity or characteristic.

DETAILED DESCRIPTION

Embodiments are directed to determining the similarity between two ormore data sets by rendering and analyzing document object model (DOM)objects associated with websites. For example, one embodiment isdirected to breaking a DOM object into a large number of data portions(i.e., “shingles”), applying a hashing algorithm to the shingles,selecting a predetermined number of hashes from the hashed shinglesaccording to a selection criteria to create a hash signature, andcomparing the hash signature to that of one or more reference pages todetermine a similarity measurement of website DOM objects of two or morewebsites. Embodiments can be used to identify phishing websites, defacedwebsites, spam websites, significant changes in the content of awebpage, copyright infringement, and any other suitable purposes relatedto the similarity between website DOM object content.

Embodiments may use a DOM object of a website in order to obtain adeeper understanding of the functionality and information containedwithin a website. For example, a web server may provide websiteinformation in response to a request for a webpage that includeshundreds of third party requests to other web servers. For instance, asocial media network page may have as many as 500 requests to variousthird party web servers while loading. The various web content servercomputers may provide dynamic and interactive content for the webpage.In such websites, the HTML code provided by the original web server mayinclude executable code (e.g., JavaScript) that when executed by a javaclient present on the computer and/or the browser can perform any numberof different functions. As such, a small amount of code can, whenexecuted, include a large amount of functionality and generate a largeamount of additional information received and provided by the websiteand any number of other websites. However, by rendering a DOM object andfully executing all of the JavaScript and other executable functionsembedded in the HTML code, a full view of the functionality of thewebsite may be obtained. For example, when rendering the DOM object, abrowser may take the HTML code and build a DOM tree. The DOM tree can beupdated which can manipulate the HTML code being executed by thebrowser.

Additionally, by hashing the content of the DOM object, HTML tags,layout of a webpage, and other information that is not captured by meredata content can be compared for similarity between websites.Accordingly, by rendering a DOM object and using the rendered DOM objectto analyze the similarity between websites, the format, layout, andinteraction options for a web browser may be compared and used todetermine a holistic view of the similarity between websites. Further,functionality and structure similarities between websites may beidentified that otherwise would not be captured by pure contentcomparison between websites.

Thus, embodiments are capable of identifying website activity that wouldotherwise be obfuscated from a website's original HTML code. Forexample, embodiments may be used to identify websites that areperforming obfuscated phishing which may include websites which useembedded executable functions within website information (i.e.,JavaScript source code) to perform the actual phishing functionality. Assuch, traditional static HTML analysis software may not be capable ofdetermining that the website is performing phishing, are similar toanother website, and/or are performing other types of similar activitiesusing obfuscated coding and/or other types of functional changes duringrendering of the webpage. As such, websites may use JavaScriptTM sourcecode and other executable code to obfuscate inputs so that traditionalphishing detection systems that do not analyze a rendered DOM would notbe capable of identifying the phishing activity or the similaritybetween websites.

Thus, embodiments provide more effective website classification andactivity detection over traditional detection systems which may notrecognize that there is any activity (due to the lack of related HTMLcode). For example, such systems may not be capable of analyzingphishing behavior for webpages that have very little HTML code andinstead may have a single script that loads in the page and dynamicallyperforms all of the activity. Thus, embodiments may be capable ofidentifying additional activity and functionality over prior art systemsdue to the use of a fully rendered DOM object and may be capable ofcomparing the similarity between this functionality between two or morewebsites.

Embodiments of the present invention are directed to an automateddiscovery and tracking tool that automatically discovers, tracks, andreports phishing websites. For example, a computer system may requestwebsite information from a web server associated with a website address,email, etc. The web server computer may send back some HTML codeincluding some JavaScriptTM or other executable functions embedded intothe HTML code. A computer's browser may then execute the JavaScriptwhich changes the HTML because the JavaScript alters the displayedinformation and the functionality of the website displayed through theHTML code by the browser. Once all of the JavaScript has been executed,a rendered DOM may be present which includes all the relevantinformation a website may be associated with and includes all theexecuted functionality included within the website information. Thus,the rendered DOM object provides a full picture of the possibleinformation accessible and available through a website. Accordingly,website information which appears to only have a limited amount ofinformation and/or content associated with it, may in fact includeJavaScript functions that are associated with a particular type ofactivity (e.g., performing phishing) and/or particular information(e.g., copyrighted and/or trademarked material) that may be associatedwith a particular entity that is interested in monitoring and/ortracking those activities and/or information.

Accordingly, embodiments of the present invention are capable ofidentifying embedded functionality within a website by executing theavailable functions, rendering a fully executed DOM object for awebsite, and analyzing the DOM object for content that is similar topreviously stored and/or classified website information. Thus,embodiments provide a deeper understanding of a website. Accordingly,the deeper understanding you can provide for a website or email (orother document/content), the more information you can compare betweendocuments and the more accurate determination of similarity betweendocuments can be provided. As such, embodiments provide a deeperunderstanding of website behavior by analyzing the full activity andfunctionality associated with a website, email, or other web-baseddocuments.

Therefore, embodiments are directed to systems that (1) collect websiteinformation from a variety of websites and web servers connected to theinterne, (2) analyze the collected data to determine whether the websiteinformation is similar to previously classified website information, (3)classify the website based on a similarity measurement to previouslyclassified website information, and (4) mediate websites and otheractors based on the classification of the website. Further, embodimentsprovide a system that can efficiently and quickly analyze a large numberof web sites to determine the similarity between a target website and alarge data set of websites faster than traditional comparison processes.Accordingly, a larger number of reference websites may be used andprocessing speeds may be maximized to ensure timely comparison of alarge amount of website information.

I. Data Collection

Embodiments may use any suitable systems and methods to find, obtain,and store website information. For example, in some embodiments, thesystem may obtain website information by crawling the Internet forwebsite information associated with particular websites and/or bycrawling through the available websites that are related and/or linkedto one another. Additionally, a particular subset of website informationmay be uploaded and/or referred to the DOM object similarity system bythird parties (partners, clients, etc.).

A. Referrals

In some embodiments, a system may obtain suspicious or known websiteaddresses and other resources from clients or through other referrals.For example, suspicious emails may be sent from clients that may includelinks to websites embedded in their email. Clients may also send rawlinks, chat or messaging resources (e.g., forwarded messages that havephishing links contained within them), and any other sources for websitelinks.

B. Search Engines

Additionally, some embodiments may crawl or discover websites usinghigh-volume sites and search engines. For example, a search engine maybe searched using keywords. Any number of search results may be loggedand used to further crawl and discover additional websites and/or otherresources.

C. Internet Crawling and Website Discovery System

FIG. 1 shows a website crawling and discovery system that is configuredto discovery website information from various sources throughout theinternet. The crawling and discover system 100 may be used to determineinternet-facing assets. For example, the system 100 may enable a user108 to generate a list of internet-facing assets (e.g., domains, hosts,etc.) that are owned by or affiliated with a particular user or businessentity (e.g., corporation). The system 100 may also enable the user 108to track specific internet-facing assets, or groups of internet-facingassets. The system 100 may be configured to automatically scan/crawlinternet-facing assets and notify the user 108 when informationassociated with an internet-facing asset changes and/or when theinternet-facing asset violates a policy specified by the user 108. Thesystem 100 may thus provide automated discovery and inventory ofinternet-facing assets, which may be faster, more accurate, and/or moreconvenient than the user 108 manually searching for and tracking suchassets, such as in a spreadsheet or other document during a yearlyaudit.

In some examples, the computing device 102 may include additionalcomponents. To illustrate, the computing device 102 may includecomponents to receive input from various devices (e.g., a keyboard, amouse, a touch screen, a network, a storage device, etc.). In additionor in the alternative, the computing device 102 may include componentsto output data or media (e.g., a video output interface, an audio outputinterface, an integrated display, a network interface, a storage deviceinterface, etc.). For example, the computing device 102 may receiveinput from a user 108 via one or more input interfaces and may outputdata or media to the user 108 via one or more output interfaces.

The memory 106 may store a discovery and inventory application 109 thatmay be executed by the processor 104. The discovery and inventoryapplication 109 may be configured to determine a list of internet-facingassets, to compile information related to the internet-facing assets,and to present the list and the information to the user 108. Thecomputing device 102 may be coupled to or in communication (e.g., via anetwork) with a discovery/inventory database 110. Thediscovery/inventory database 110 may store data (e.g., results,settings, etc.) associated with the discovery and inventory application109.

The computing device 102 may be in communication with the internet 111.The computing device 102 may communicate with a domain name system (DNS)response database 114. The DNS response database 114 may store capturedDNS messages. The captured DNS messages may include records linking adomain name to one or more internet protocol (IP) addresses (e.g., asingle IP address or an IP address block). In some examples, thecaptured DNS messages may include records (e.g., canonical name (CNAME)records) linking domain names to domain names.

The computing device 102 may be in communication with a border gatewayprotocol (BGP) database 115 (e.g., a BGP enabled device). The BGPdatabase 115 may store mappings between autonomous system numbers (ASNs)and IP addresses. The BGP database 115 may support ASN queries thatinclude ASN(s) and result in an answer including an IP address, multiplenon-contiguous IP addresses, and/or a block of contiguous IP addresses.The BGP database 115 may also support reverse ASN queries that includeIP address(es) and result in an answer including ASN(s).

The computing device 102 may be in communication with a whois database116. The whois database may store information linking an IP address, anIP address block, or a domain name to a whois contact (e.g., a contactname, a physical address, a mailing address, an e-mail address, or acombination thereof). The whois database 116 may support whois queriesthat include a domain name, an IP address block, or an IP address andresult in an answer including a whois contact. The whois database 116may support reverse whois queries that include a whois contact andresult in an answer including a domain name, an IP address block, or anIP address.

In the illustrated example, the computing device 102 communicates withthe DNS response database 114, the BGP database 115, and the whoisdatabase 116 via the internet 111. In other examples, the computingdevice 102 may be directly coupled to one or more of the databases114-116, the computing device 102 may be in direct communication withone or more of the databases 114-116, or the computing device 102 maycommunicate with one or more of the databases 114-116 via a differentnetwork or combination of networks, which may include public network(s)and/or private network(s).

A first domain 118 may be coupled to the internet 111 via a first accessnetwork 112. The first domain 118 may be mapped (e.g., via DNS) to oneor more IP addresses (e.g., a first subnet represented in CIDR notationas 192.0.2.0/24). The first domain 118 may have an associated domainname, such as “example.com.”

It should be noted that although not shown in FIG. 1, the first domain118 may include one or more sub-domains. The first domain 118 may alsobe a sub-domain of a larger domain. The first domain 118 may map to oneor more IP addresses (e.g., via DNS), where each IP address isassociated with a host. As used herein, a host may include generalpurpose computers, as well as other devices, that have an IP address.For example, a host may include a printer or other internet enableddevice.

In the illustrated example, the first domain 118 maps to IP addressesassociated with one or more first hosts 119. Each of the first hosts 119may have an associated hostname (e.g., firsthost.example.com). Ahostname may also be referred to as a fully qualified domain name(FQDN). In some examples, a host may have more than one IP address(e.g., have more than one network interface or have more than one IPaddress per network interface), and one or more of these IP addressesmay not be mapped to the first domain 118. For example, a particularcomputing device may have two IP addresses. One of the IP addresses maymap to a first hostname (e.g., firsthost.example.com) and another of theIP addresses may map to a second hostname (e.g., firsthost.example.net).Thus, a particular host device may belong to more than one domain.

One or more of the first hosts 119 may include (e.g., execute) a DNSname server. For example, the first hosts 119 may include a first DNSname server 120. The first DNS name server 120 may include DNS records121. The DNS records 121 may link a domain name to one or more internetprotocol (IP) addresses. In some examples, the DNS records 121 mayinclude records (e.g., CNAME records) linking domain names to domainnames. The DNS records 121 may correspond to the first domain 118. Forexample, the DNS records 121 may store mappings between a hostname ofeach of the first hosts 119 and a corresponding IP address. In someexamples, the DNS records 121 may further include information regardingone or more other domains, such as a second domain 122 (e.g.,“example.org”). The DNS records 121 may indicate that the first DNS nameserver 120 is an authoritative name server for one or both of the firstdomain 118 and the second domain 122. Some or all of the DNS records 121may be stored in the DNS response database 114.

The second domain 122 may be coupled to the internet 111 via a secondaccess network 113. The second domain 122 may be mapped (e.g., via DNS)to one or more IP addresses (e.g., second subnet represented in CIDRnotation as 198.51.100.0/24).

It should be noted that although not shown in FIG. 1, the second domain122 may include one or more sub-domains. The second domain 122 may alsobe a sub-domain of a larger domain. In the illustrated example, thesecond domain 122 is mapped to IP addresses associated with one or moresecond hosts 123. Each of the second hosts 123 may have an associatedhostname (e.g., secondhost.example.org). In some examples, a host mayhave more than one IP address (e.g., have more than one networkinterface or have more than one IP address per network interface), andone or more of these IP addresses may not be mapped to the second domain122. For example, a particular computing device may have two IPaddresses. One of the IP addresses may map to a first hostname (e.g.,secondhost.example.org) and another of the IP addresses may map to asecond hostname (e.g., secondhost.example.net). Thus, a particular hostdevice may belong to more than one domain.

One or more of the second hosts 123 may include (e.g., execute) a DNSname server. For example, the second hosts 123 may include a second DNSname server 124. The second DNS name server 124 may include DNS records125. The DNS records 125 may link a domain name to one or more internetprotocol (IP) addresses. In some examples, the DNS records 125 mayinclude records (e.g., CNAME records) linking domain names to domainnames. The DNS records 125 may correspond to the second domain 122. Forexample, the DNS records 125 may store mappings between a hostname ofeach of the second hosts 123 and a corresponding IP address. In someexamples, the DNS records 125 may further include information regardingone or more other domains, such as a third domain (e.g., “example.net”).The DNS records 125 may indicate that the second DNS name server 124 isan authoritative name server for one or both of the second domain 122and the third domain. Some or all of the DNS records 125 may be storedin the DNS response database 114.

Additional details of such a discovery system may be found in U.S.Non-provisional application Ser. No. 14/520,029, filed Oct. 21, 2014,which is hereby incorporated by reference in its entirety for allpurposes.

II. Data Analysis

Once the system has obtained website information associated with anumber of different providers, entities, etc., the system may generate ahash signature for each of the identified websites and may store each ofthe websites with a predetermined classification and/or index associatedwith the hash signature of the website.

FIG. 2 shows a similarity system 200 that is configured to determine thesimilarity between a website and a set of reference websites andclassify the target website based on the similarity. The computingdevice 210 may comprise a processor 211 and a computer-readable memory212. The memory 212 may comprise a website interface module 213, a DOMrendering module 214, a data portion separation module 215, a hashsignature generation module 216, and a website classification module217. The computer device 210 may be coupled to a discovery/inventorydatabase 218 and a classified hash signatures database 219. Note that insome embodiments additional modules may be implemented and in otherembodiments the functionality of each module may be combined into fewermodules than shown in FIG. 2. The functionality of the modules 213-217and the information contained within the databases 218-219 are describedin further detail below.

The computing device 210 may be coupled to one or more website servercomputers 220 and one or more website content server computers 230A-230Dthrough a communication network 240 (e.g., the Internet). The computingdevice 210 may obtain website information (e.g., HTML code) from thewebsite server computer 220 (which may be identified through thediscovery/inventory database or through any other suitable method). Thewebsite information may include functions and/or instructions that whenexecuted by a browser application or other modules on the computingdevice 210 cause the computing device 210 to contact, get informationfrom, and/or post information to the one or more website content servercomputers 230A-230D. Additionally, the computing device 210 may obtaininformation from the one more website content server computers 230A-230Dwhile executing the one or more functions embedded in the websiteinformation. For example, some websites may request information frommultiple different server computers that store content, media, and/orany other relevant information that is meant to be displayed, used,and/or processed for a website 220 that originated the initial websiteinformation to be displayed and interacted with by a user of thecomputing device 210.

The computing device may be used to implement multiple differentfunctionality and embodiments. For example, the computing device may beconfigured to generate hash signatures for a website, classify the hashsignature for one or more classifications, and store the hash signaturein one or more databases to be used in classifying future websites basedon the similarity of those websites to the stored database (alsoreferred to as a corpus) of websites. Furthermore, in some embodiments,the computing device may be configured to obtain website information,determine the similarity of websites to a set of hash signaturesassociated with previously hashed DOM objects for known websites, andclassify the website based on the similarity to previously classifiedknown hash signatures.

A. Hash Signature Generation, Classification, and Storage

Once the internet crawling and discovery system has obtained websiteinformation associated with a number of different providers,corporations, entities, etc., the computer device may generate a hashsignature for each of the identified websites and may store each of thewebsites with a predetermined classification and/or index associatedwith the hash signature of the website.

FIG. 3 shows an exemplary process of classifying DOM object hashsignatures as being associated with particular classifications. Forexample, the computing device may be configured to obtain websiteinformation associated with a discovered website, render a documentobject model (DOM) object of the website using the website information,generate a hash signature for the rendered DOM object, classify thewebsite and corresponding hash signature as being confirmed and/ordismissed as part of that classification, and store the hash signatureof the website as being associated with one or more classifications.Accordingly, embodiments may be used to build a corpus of classifiedhash signatures that may be used to identify similarities and classifylater discovered websites.

At step 301, the website interface module of the computing device mayobtain a website address associated with a web server computer andcontact the website. As described above, the computing device may obtainthe website address through any suitable method including crawling anddiscovering websites using the system described above in reference toFIG. 1. The website addresses may be stored in a discovery/inventorydatabase 218 of website information, rendered DOM objects, and/oraddresses associated with discovered websites. For instance, the websiteinterface module may obtain a website address from thediscovery/inventory database associated with the crawling and discoverysystem and may use the address to contact the web server computer thatis identified through the website address.

At step 302, the website interface module of the computing devicereceives website information from a web server corresponding to awebsite. The website interface module may send a request for the website information using any suitable request functionality and/or dataprotocol for the website and may receive a response including thewebsite information using any suitable communication protocol. Forinstance, the website interface module may send a request using HTMLcode and may receive a response from the web server including therequested website information configured to be returned for therequested website address.

At step 303, the DOM rendering module of the computing device renders adocument object model (DOM) object of the of the website using thewebsite information. The DOM rendering module may execute any and allfunctional code within the website information in order to obtain afully executed DOM object. In some embodiments, the functional codewithin the website information may instruct the DOM rendering module torequest and post information to one or more website content servercomputers 230A-230D.

At step 304, the hash signature generation module 216 of the computingdevice 210 generates a hash signature of the rendered DOM object. A hashsignature may be obtained by applying one or more hash functions to therendered DOM object. Because the rendered DOM object includes bothwebsite content (e.g., text) and code information related to webpageformat (e.g., HTML tags) and meta data, and the DOM object is formattedaccording to the positional relationships between information (e.g., theDOM object is nested according to the relationship between content), theDOM Object may provide a much different configuration and formatting ofinformation than a purely website content based analysis of the websiteinformation. Accordingly, hash signatures using the DOM object may havevery different results than a hash function that is applied to websiteinformation that is not rendered into a fully executed DOM object. Thespecific steps for generating a hash signature are explained in furtherdetail below for some embodiments but one of ordinary skill would easilyunderstand different types of hashing algorithms and hash signaturegeneration processes that could be implemented.

At step 305, the website classification module of the computing deviceclassifies the website and the corresponding hash signature of thewebsite's DOM object as confirmed or dismissed for one or moreclassifications. In some embodiments, during an initial classificationprocess where a corpus of confirmed and dismissed websites for eachclassification have not yet been established, a human operator may beused to analyze each of the websites and classify the websites as beingof a particular class. Additionally and/or alternatively, in someembodiments, the system may receive indications of the classificationfrom third party referrals, clients, and/or any other entities and maystore the hash signatures as being associated with those classificationsbased on the reported classifications associated with each website. Assoon as a corpus of stored websites has enough samples of both confirmedand dismissed hash signatures within the database, the system may thenuse the similarity between websites to automatically classify thosewebsites as being confirmed or dismissed as a member of thatclassification using a similarity measurement and similarity threshold.The automatic classification based on similarity measurement will bedescribed in further detail below.

At step 306, the website classification module of the computing devicestores the hash signature as being associated with the one or moreclassifications based on the classification of the website. As describedabove, the classification as a confirmed or dismissed member of aparticular classification may be performed by a human operator initiallyand then may be automated using similarity measurements once a corpus ofwebsites within a particular classification has been built. Additionaldetails regarding the storage of the hash signatures of the DOM objectsinto confirmed and dismissed hash signatures associated withclassifications will be described in further detail below.

1. Rendering a Fully Executed DOM

As explained above in reference to step 303 of FIG. 3, the DOM renderingmodule may be configured to render a DOM object associated with thereceived website information. The process of rendering a DOM object mayinclude executing functions included in the website information. Thefunctions may include JavaScript and other executable code (e.g.,SilverLight™, etc.) that instructs a computer to perform particularfunctionality, obtain information from one or more other servercomputers connected through the communication network (e.g., obtainmedia content from a third party computer system), as well as sending(or posting) information to one or more other server computers connectedthrough the communication network. As the functions are executed, thefunctions may obtain additional HTML code from the other computersand/or may trigger conditions within the existing website informationthat may result in changes to the existing DOM object. As such, the DOMobject may change and may be updated with new HTML code, receivedinformation, prompts, media, etc., as the functions are executed. Thus,as the computing system executes each of the functions embedded in theHTML code, the DOM object is altered until all of the availablefunctions are executed within the website information. At this point,the DOM object may be considered fully executed and rendered.

Using a fully executed rendered DOM object is advantageous because nomalicious third party software or functionality can be hiding within thewebsite. Thus, the true nature of the website and the functionalityassociated with the website information can most accurately bedetermined once all functionality within the website information isexecuted and a fully rendered DOM object is obtained.

FIG. 4 shows an exemplary DOM object and a corresponding exemplary HTMLcode response body. As can be seen in FIG. 4, although there is a lot ofoverlap between the HTML code response body 420 that is received fromthe server computer and the DOM object 410, the DOM object 410 has anested or tree data format that shows the relationship betweeninformation within the HTML response body 420. Additionally, the DOMobject 410 includes additional information that is not shown in the HTMLcode response body 420 including meta data and source web addressinformation.

FIG. 5 shows the changes to the DOM object as the functions containedwith the website information are executed. For example, attributes andelements may be added, removed, altered, and/or any other suitablechanges may be provided through execution of functional code within thewebsite information. The changes shown in FIG. 5 are illustrative onlyand any rendering action may include hundreds or thousands of changesdepending on the complexity and functionality embedded in the functionsembedded into the website information.

2. Generating a DOM Object Hash Signature of a Website

As explained above in reference to step 304 of FIG. 3, the hashsignature generation module 216 of the computing device 210 may beconfigured to generate a hash signature of the rendered DOM object. Oneexemplary method of generating a hash signature is provided below butone of ordinary skill in the art would recognize that different methodscould be used to generate different types of hash signatures. As long asthe hash signatures are generated in a consistent basis across thevarious websites that are analyzed and processed, different methods maybe used to generate hash signatures that can be compared to determinesimilarity between websites.

a) Separating the DOM into Data Portions—Website Shingling

First, a DOM object may be separated into a plurality of smaller fixedsize data portions to ensure that smaller portions of data within thewebsite information may be compared between documents. For example, adata portion separation module may process a rendered DOM object tobreak the rendered DOM object into a plurality of consistent fixed sizedata portions that can be compared between websites. In someembodiments, the data portion separation module may be referred to as a“shingler” and the separated data portions may be referred to as ashingles of the DOM object.

FIG. 6 shows an exemplary method of separating a document into aplurality of data portions of fixed length (i.e., shingles). As shown inFIG. 6, the document may be any input information into the data portionseparation module. For example, the document may be the fully renderedDOM object that is generated by the DOM rendering module in step 303 ofFIG. 3. The data portion separation module may break the document into aplurality of consistently sized, fixed, data portions by taking a fixedlength sample of the content within the document. The shingle length 612may be the fixed length size of the samples that the document may beseparated into in order to generate the shingles.

For example, FIG. 6 shows a very simple implementation that takes asimple word “hello” and a shingle size of 2 and generates 4 differentshingles of shingle length 2 by breaking the document (i.e., “hello”)into equal fixed length shingles of shingle length 2. Thus, by taking a2 letter sample of the word “hello,” the equal fixed length dataportions “he”, “el”, “ll”, and “lo” are created. The data portions arecontinued to be created until the last shingle of length two is possible(i.e., “lo”). Accordingly, the document “hello” has been broken into 4shingles of length 2. Although this is a very simple sample with only 4resulting shingles, the separation of a DOM object that has thousands ofcharacters within the DOM object could easily result in hundreds ofthousands or more shingles for a rendered DOM object.

Choosing the size of the shingles for the documents being analyzed isimportant in order to capture the similarity between documents and toavoid over-sampling. The shingle length of 2 shown in FIG. 6 is purelyfor ease of example but one could imagine a large document, such as ajournal or newspaper article, and a shingle size of 2 would result in avery large number of shingles. Where the shingles size is 2, shingleswill be created for every two characters. For a suitably large document(e.g., a DOM object of a website), if shingles of size 2 were comparedto other large documents, any similarity analysis would find that therewould likely be significant overlap leading to the conclusion that twodocuments are identical or very similar—when in fact, the shingle sizeparameter was not tuned well for the size of the document, leading tofalse positives. Accordingly, shingle size may be selected to optimizethe similarity measurement calculations between documents of relativelythe same size.

For example, where a shingle size is 1, comparing the set of shinglesgenerated by two different sentences: 1) “The quick brown fox jumps overthe lazy dog” and 2) “abcdefghijklmnopqrstuvwxyz,” would produce a setof shingles that indicate that the two documents are exactly equal,which is a false positive. Thus, the shingle size parameter should bechosen carefully depending on the application.

In English, the average word is 5 letters long. For short documents,choosing 5 or 6 characters as a shingle size is a viable choice, whilelonger documents would benefit from double the word length. Optimalshingle size will vary based on language and word length. While Englishword length was used as an example, other alphabets and tokens can justas easily be used.

Given a properly chosen shingle size, the shingling of a document canencode both the ordering and the content of the underlying text. Usingthese sets, a Jaccard Similarity can be computed by comparing the numberof shingles that are shared between sets divided by the number ofshingles that are not shared between sets. The Jaccard Similairty cangive a fractional similarity between the two documents. Thus, given theshingles of two documents, with properly chosen parameters, we couldcompute their set intersection to determine the similarity measurebetween the documents.

However, as the document size grows, so does the number of shinglesrequired to represent the document. Thus, the calculation and comparisonof the number of shared elements over the number of unique elements canbecome an onerous calculation that can take very long processing times.

However, if the shingles can be sampled to still accurately reflect thecontents of the document, the document size can be compressed togenerate a signature that is much smaller to determine documentsimilarity. Thus, by sampling these shingles using a sampling hashingalgorithm (e.g., a MinHash), the length of the documents needed toestablish the similarity between documents can be minimized and theprocessing of the similarity process can be much faster than a one toone comparison of shingles across documents.

b) Converting Shingles to Numeric Values

In order to convert the shingles to an integer value which can befurther hashed by universal hash functions, a suitable hash function maybe used that is both quick and has a low collision rate. For example, onorder to apply a MinHash algorithm, first the shingles may be convertedfrom a shingle string to an integer hash value, which can be furtherapplied to a universal hash functions. For example, a djb2 a hashfunction, known for few collisions and fast computation may be used.Additionally and/or alternatively, a MurMurHash may be used which isalso capable of producing hashes very quickly and with low collisionrates.

c) Applying Universal Hash Functions

FIG. 7 shows an exemplary diagram of a universal hashing function.Universal hashing is the process of choosing random parameters for aclass of hash functions. In this example, the universal hash function iscomprised of a and b, which are random numbers, p is a large prime, andN, which serves to divide the space further into buckets. MinHashingrequires using collections of hash functions which should producenon-colliding results. Randomly generating a and b multiple times willcreate a family of hash functions suitable for sampling when applying aMinHash.

The universal hash function with input parameters generates a functionH(x), which given an x, the function computes the hash value. But, givenrandom values for a and b, and a large prime p, we need to determine ifthis hash function has desirable properties. Thus, the universal hashfunction should be applied with selected parameters that ensure a lowcollision rate and random value distribution. Accordingly, when applyingparameters to the universal hash function, a result should hash integersthat are distributed approximately evenly into hash buckets. Thus,universal hash functions should be tested before being applied to alarge sample input to ensure that the hash values are distributed evenlyand to minimize collision of hash values.

d) Hash Collections Of Universal Hash Functions

FIG. 8 shows a MinHash algorithm that includes a hash collection ofuniversal hash functions for generating hash values from shingles, andsampling those values. The collection of hash functions within theMinHash algorithm may accept an initial seed and the number of hashes tobe generated. Using the same initial seed for the hash collections ofuniversal hash functions allows the hash functions to reproduce a randomsampling in a fixed manner. Thus, when the hash collection of universalhash functions are applied to a given input, a predetermined number ofhash values are returned which correspond to hashing the shingle valuethe predetermined number of times.

For example, in FIG. 8, the universal hash function is applied 4different times to generate 4 different hashes of each input value.Thus, as shown in box 813, by applying the shingles from FIG. 6 above tothe hash collection of the universal hash functions, 4 different hashesare generated for each input value. For instance, for the input “he”,the hashes −10, 25, 33, and −97 are generated. These various hash valuesare generated by applying random variables to the universal hashfunction to generate 4 different universal hash functions using the sameseed value such that they are consistent but have some random variation.Accordingly, single hash values are generated for each hash algorithmusing the same input value “he”. This generates 4 different hashes forthe single input that can then be sampled consistently to create a hashsignature. Thus, each shingle will be hashed N times and sampled togenerate a hash signature.

Any number of hash functions may be applied and the use of 4 in theexample shown in FIG. 8 is used for simplicity of example. Usually avalue of 100 or 500 may be applied as a suitable number of hashes togenerate a good sampling of hashes for the signature. However, thenumber of hashes to be applied depends on the target application andshould be tuned appropriately for that application. Generally speaking,the higher the threshold required for matching, the more hash functionsthat should be applied to attain a threshold similarity.

e) MinHash And Shingle Sampling

Using the collection of hash functions, each shingle is evaluated byinputting each shingle into the N hash functions, producing N hashedvalues. The MinHash then chooses the minimum hash value for each shinglewhich represents the signature for the shingle out of the N generatedhash values. In the case of the MinHash a selection criteria may beapplied such that the minimum value for each hashing algorithm may besampled from the hashes and chosen for the hash function. The resultingset of hashes of shingle values is comprised of the minimum hashcomputed for a shingle which was chosen from the N hash functions. Asshown in FIG. 8, for each column of hash values, the minimum is chosen.The resulting vector of minimum hash values 815 represents the MinHashsignature of the document.

It should be noted that the minimum hash value chosen by the MinHashfunction is by convention and that any consistent sampling function orselection criteria could be implemented. For example, a MaxHash could beapplied such that the maximum value could be sampled and the MaxHashalgorithm would still produce a document signature that would only matchif two documents had high similarity. Thus, if the function chosen tosample the hash values is consistent, the similarity between documentsmay be calculated and compared. For simplicity, the embodimentsdescribed herein apply a MinHash but any suitable sampling includingmaximum or median sampling may be used.

Thus as shown in FIG. 8, one embodiment may apply a MinHash whichcreates a document signature by creating the shingles of the document,hashing each of those shingles N times, and from each position, in eachhash, taking the minimum value. Note, the minimum value in each columncorresponds to the hash value being sampled.

f) Locality Sensitive Hashing And MinHash Sampling

Additionally, in some embodiments, further sampling may be applied usingLocality Sensitive Hashing (LSH). FIG. 9 shown an exemplary process ofapplying a LSH hashing algorithm to the result of the MinHash process tofurther sample the results and further minimize the amount ofinformation necessary to determine the similarity between documents. LSH910 is an algorithm which samples the results of the MinHash hashsignature 912 and compresses the MinHash signatures into LSH buckets913. The additional sampling serves to further reduce the size of thenumber of features that need to be compared to determine if documentsare candidates for being similar. LSH relies on the principle that ifdocuments are similar they should hash to approximately the same value.So, given some similarity threshold and N hash functions, a MinHashfunction may be sampled in such a way that two documents are candidatepairs for similarity if and only if at least one of their LSH bucketsare identical and share the same offset within the signature.

Thus, LSH quickly compares documents that are potential candidatematches to determine whether a closer analysis should be completed.Accordingly, LSH allows for large numbers of documents to be comparedquickly such that if the LSH between documents is not in the samebucket, processing will be avoided to focus on those documents that arewithin a similar bucket and thus, are more similar. Thus, by using LSHvalues as buckets, the system can determine potential candidate pairs inorder O(n) time by binning those LSH values that match together. Thus,the system can quickly determine potential candidates and analyze thesimilarity measurement for only those website hash signatures that arein the same LSH bin.

g) LSH Hashing

As shown in FIG. 9, an LSH algorithm may be applied to the MinHash HashSignature generated in FIGS. 6-8 to hash together groups of valuesaccording to a predetermined number of bands. The LSH hashing algorithmmay have a number of bands parameter that is an input to the LSHalgorithm and that produces a number of LSH buckets, which are asampling of the MinHash values in the hash signature.

A key part of the LSH algorithm is the number of bands 911 input. Thebands 911 in this algorithm subdivide the hash signature into N/numberof bands, where each subdivision is then hashed to represent an LSHbucket 913. Thus, the number of LSH buckets 913 may be determined by thenumber of hashes in a hash signature 912 divided by the number of bands911. For example, as shown in FIG. 9, the hash signature has 4 values,the number of bands is 2, so the hash values are subdivided into twogroups of 2 hash samples each. The 2 hash values in each two groups arethen hashed to generate a single hash for each group (e.g., 13, −37)which are determined to be the LSH buckets 913 for a hash signature 912.

Note that the number of bands affects the processing resources andaccuracy of the similarity between those documents found in LSH buckets.For example, in order to get a 100% match for the buckets of the LSHfunction and the resulting similarity, an equal number of bands and hashvalues may be used. However, if a 50% threshold is implemented, fewerbuckets can be used and the number of bands may be minimized. This willresult in less processing resources being used. Accordingly, there is atradeoff between accuracy and efficiency. Thus, as the accuracythreshold is lowered, the efficiency of comparing the LSH bucketsincreases. For example, where a 50% similarity is set, there may be 4buckets to compare, while at 99% similarity, there are 50 buckets tocompare. Accordingly, in choosing values of similarity close to 100%,documents returned will be nearly identical, while values oft close to0.5 or 0.6 would capture documents that are contained within otherdocuments. Thus, when designing the hashing algorithms, a tradeoffbetween processing resources, similarity of documents, and number ofhashes in the signature may be designed to the particular websites orother applications being implemented.

3. Classifying and Storing a DOM Object Hash Signature of a Website

Once a hash signature has been generated including either, for example,a MinHash value or a LSH sampling buckets, or both, the hash signaturemay be classified and stored in a classified hash signatures database asbeing confirmed or dismissed for one or more different classifications.

FIG. 10 shows an exemplary website hash signature database 219 thatincludes DOM object hash signatures of websites that have beenclassified in some exemplary classifications. FIG. 10 includes someexemplary classifications that may be used in an exemplary embodiment.For example, classifications may include phishing classifications,trademark and/or copyright classifications, website revisionclassifications, and offensive content classifications. Note that theseare non-limiting exemplary classifications and any other website contentand/or formatting may be used to identify additional classifications foruse with the website similarity identification system.

Each of the classifications may include confirmed and dismissed (i.e.,not confirmed) examples of DOM object hash signatures that can be usedto compare to a target website hash signature to determine whether ahash signature is confirmed or dismissed as part of that classification.There may be a similar number of hash signatures within each orconfirmed and dismissed hash signature classifications or there may bemore confirmed or dismissed examples of hash signatures within theclassified hash signatures for each of the classifications 410-440.Additionally, hash signature samples for the classifications may bebuilt up with mores samples as more websites are rendered, hashed,analyzed, and compared to the stored classified hash signatures. Thus,accuracy may improve over time as more confirmed and dismissed hashsignatures are stored for each classification. Furthermore, a differentsimilarity threshold associated with each classification may be storedassociated with each classification such that some types ofclassifications may have different magnitudes of similarity measurementsbetween them before a website is considered similar to a confirmedand/or dismissed hash signature for a particular classification.

The similarity threshold may be dependent on the type of activity beinganalyzed. For example, the website revision classification may have avery high similarity threshold because the websites are differentversions of one another and thus may be very similar generally. Thus, ahigh threshold may be required to be confirmed to ensure that thewebsites are in fact the same and/or that the differences are very minorcompared to the phishing functionality classification which may belooking for less similarity between websites (i.e., looking for a smallsection of the website that is performing a particular function).

Note that the classified hash signatures may be organized in thedatabase in any suitable manner. For example, when a hash signature of awebsite is stored in the database, each hash signature may be assignedto one or more classifications. For example, in some embodiments, asingle hash signature may be classified as confirmed as a phishingwebsite but may be classified as dismissed for a particular brand'scopyright, a particular website's revision history, and/or for offensivecontent classification. Thus, the similarity identification system mayhave a variety of dismissed and confirmed samples for each of theclassifications. Additionally and/or alternatively, in some embodiments,the hash signature of a particular website may be stored within aparticular classification database as confirmed or dismissed due toparticular formatting and/or content features of the website. Thus, someembodiments may stricter about which hash signatures are included in thedismissed websites within each classified hash signature classification.

Phishing classifications may include hash signatures for websites thatare confirmed 1010A and/or dismissed 1010B as performing phishingfunctionality. The confirmed hash signatures may be websites that havebeen independently confirmed as having phishing website formatting,functionality, a particular common web server associated with thephishing functionality, and/or through any suitable method foridentifying confirmed associations with phishing functionality. Thedismissed hash signatures 1010B may include websites that have beenanalyzed and do not include any such functionality. Hash signatures froma broad base of different types of websites may be included in theclassification to allow a wide-range of different similar material to beidentified between the dismissed 1010B and confirmed 1010A hashsignatures of websites.

Trademark and/or copyright classifications 1020 may include hashsignatures for websites that are confirmed and/or dismissed as havingparticular trademarks, formats associated with a trademark associatedwith a particular entity, have particular formatting and functionalityassociated with a particular trademark and/or copyrighted work, etc. Forexample, if a company provides their websites in a particular formatand/or having particular content that is unique, websites having beenconfirmed as having that format and/or content may be stored in aconfirmed classification associated with that trademark and/orcopyright. However, websites that are dismissed as not having thatformat, content, and/or use of that particular copyrighted ortrademarked work may be stored as having a dismissed status within thatclassification.

Website revision classifications 1030 may include confirmed hashsignatures 1030A and dismissed hash signatures 1030B for websites beingassociated with a particular version of a website. For example, hashsignatures for one or more versions of a particular webpage may bestored as being confirmed 1030A as being the same or a slightlydifferent version of that website. The website may be regularly orperiodically sampled, rendered into a DOM object, have a hash signaturegenerated of that rendered DOM object, and may be compared to theconfirmed previous hash signatures associated with that website todetermine whether a sufficient change has occurred to move the hashsignature into being similar to dismissed hash signatures associatedwith that website or any other websites not similar to that version ofthe website. If so, the web domain operator may be notified of thechange since the changes are substantial enough to show that thesimilarity threshold no longer is similar to the previous versions ofthat webpage. Accordingly, the dismissed hash signatures associated withthat classification may be selected from different webpages from thatclient and/or webhost or random webpages not associated with thatprovider. In some embodiments, the dismissed web pages may be samples ofother providers that have been hijacked, hacked, and/or otherwisechanged such that the same changes may show as the most similar, leadingto a notification that the webpage has been altered in a similar manner.

Offensive content classifications 1040 may include confirmed hashsignatures 1040A and dismissed hash signatures 1040B for websites thatinclude particular types of offensive content including particular mediaor content, formatting of websites that have been altered to providefake pharma selling offers and/or other functionality, and/or any otheroffensive material that a webhost may want to be notified about if theirwebpages are found to be serving that type of material. Accordingly, thedismissed pages may include hash signatures for websites that do notinclude that type of functionality while the confirmed hash signaturesmay be previous types of websites that have been confirmed as havingsuch functionality embedded within the website.

B. Methods of Identifying Similarity Between Websites

Once one or more hash signatures have been generated, classified, andstored for one or more websites in the classified hash signaturedatabase, embodiments may use the classified hash signatures to identifysimilarity between a target website and the stored hash signatures ofthe classified websites. For example, as described above, the computingdevice of FIG. 2 may generate a hash signature by taking data portions(i.e., shingles) of a website, applying one or more hashing algorithmsto the data portions, and comparing them piece-wise.

For example, applying a MinHash algorithm to a rendered DOM object for awebsite and comparing the resulting hash signature to one or more hashsignatures generated by applying the MinHash algorithm to other DOMobjects associated with those websites is one method of comparing twoweb pages for a level of similarity. A system may store a classificationof website information in a corpus and may compare an unknown website toa known website of a particular classification to determine whether theunknown website may be the same type of website and/or the likelihoodthat the unknown website contains some information that is stored in thecorpus (e.g., copyright infringement) by how similar it is to the knownwebsite features. The MinHash algorithm samples a document (e.g., arendered DOM object of a webpage) in a consistent way so that the systemis comparing shingles (i.e., data portions) across multiple documents.The system may then look for common hash values for those shinglesacross multiple hash signatures of websites to identify a similaritymeasurement between hash signatures.

As described above in reference to the hash collections being applied,the system may take the minimum hash value for each of the specific hashfunctions applied to the shingles and repeat that process many times.For instance, the system may have a hundred different hash functions andmay create a MinHash signature that would be have one hundred hashes ina signature, where each of the selected values includes the smallesthash value for each hash function applied to the plurality of dataportions. Thus, applying the MinHash algorithm allows for a consistentlength for a hash signature and selects the hash value for each hashfunction of the multiple hash functions through a consistent process.This will result in repeatable sampling of values when the same contentis found within a document.

For example, the system may sample a set of 10,000 shingles in adocument and select a hundred of those based on the lowest hash valuesassociated with the 10,000 shingles applied to each of 100 differenthash functions that are created with the same initial seed. The processis applied consistently across multiple documents so that when a hashvalue associated with one of the shingles is selected, the sampled hashvalue ends up being consistently found across multiple documents becauseit is the minimum value of one of the hash functions that is appliedacross multiple documents. Thus, the consistency of the method allowsthe system to consistently find the same minimum hash value (and thusthe corresponding shingle) across multiple documents. Thus, the systemcan determine another webpage included the same shingle because it endedup being the minimum value for the same hash function. Thus, the secondwebpage had the same shingle because it also ended up as being a minimumvalue for the same hash function number. Because the hash values forthat shingle are the same across the two pages, the system knows thatthere is at least some similarity in the document as the same shinglewas found in both documents. Once you have determined the number ofmatching hash values between two websites, you can divide the number ofshared hash values divided by the total number of hash values to get asimilarity measurement between webpages. This provides an approximationof the similarity between two web pages. A similarity threshold may thenbe compared to determine whether the webpages are similar enough for atype of classification to be considered as having the same type ofclassification.

FIG. 11 shows an exemplary method of identifying the similarity betweenwebsite information in order to classify a website. At step 1101, awebsite interface module of the computing device receives websiteinformation from a web server corresponding to a website. The websitemay be contacted through any suitable method. For example, the websitemay have been referred to the computing system as a potentiallyinteresting website and/or the website may have been contacted as partof a search strategy related to a particular type of content, keywordsin the website information, being associated with a particular set ofwebsites, and/or through any other suitable methods.

At step 1102, the DOM object rendering module renders a document objectmodel (DOM) object of the website using the website information. Asdescribed above in section II(A)(1), the DOM object may have anyfunctionality within the page fully executed so that the fullfunctionality of the website is captured by the DOM object.

At step 1103, the data portion separation module separates the DOMobject into data portions of a fixed length. The data portions may havea fixed length of any suitable length depending on the length of therendered DOM object. Typically, the data portions of the DOM object mayhave a fixed length somewhere in the range of 10 characters and may havetens of thousands data portions.

At step 1104, the hash signature generation module applies a hashingfunction to each of the data portions to generate hash values for eachof the data portions. For example, in some embodiments, applying thehash function includes applying a predetermined number of hashingfunctions to the plurality of data portions such that a number ofdifferent hashing values are generated for each of the plurality of dataportions. The predetermined number of hashing functions may be generatedusing a common seed value and using at least one randomly generatedinput variable to generate sufficiently different hashing algorithmsbetween the predetermined number of hashing functions. Thus, by applyingthe plurality of different predetermined number of hashing functions, apredetermined number of values for each of the plurality of dataportions is generated. For example, for a DOM object that has 10,000data portions, 10,000 different hash values may be generated for each ofthe plurality of hash functions.

At step 1105, the hash signature generation module creates a hashsignature by selecting a predetermined number of hash data portionsusing a selection policy. A selection policy may be repeatable andconsistent rule that can be applied across multiple different numbers ofdata portions for a document and types of data. For example, theselection policy may include a minimum value, a maximum value, and/or amedian value for the set of hashed data portions.

For example, for a MinHash algorithm, the selection policy may includetaking the minimum hash value for each hashing function. Thus, where thepredetermined number of hashing functions includes 100 different hashingfunctions being generated, the selection policy may select a minimumhash value from the 10,000 hash values generated by applying one of thehash functions. The result would be one of 100 different minimum hashvalues selected by the selection policy and the selection process may berepeated for each of the 100 hash functions applied to the 10,000 dataportions. Thus, a hash signature may be generated including 100 of theminimum hash values of the 10,000 data portions for each of the 100different hash functions applied to the 10,000 data portions.Accordingly, the hash signature may include 100 hashing samples of amuch larger document (e.g., 10,000 data portions) that may be repeatedlyfound in other documents that apply the same hashing algorithms andselection process across documents.

At step 1106, the website classification module compares the hashsignature of the DOM object to a known hash signature of a DOM objectgenerated from another website that is associated with a firstclassification. The comparison may include comparing each of theplurality of hashed data portions within the hash signature to aplurality of known hashed data portions of the known hash signature. Foreach hash signature that matches, the system may determine that dataportions within the target website and the classified website are thesame. This process may be repeated until all of the hashed data portionswithin the hash signature are compared to the hash signature of theknown DOM object hash signature.

At step 1107, the website classification module calculates a similaritymeasurement between the hash signature of the DOM object and the knownhash signature of the DOM object generated from the other website. Thesimilarity measurement may include a fraction of the total number ofmatching hash values divided by the total number of hash values withinthe hash signature. For example, using the example described above, fora hash signature that has 100 hash values with 32 matching selectedhashes, the similarity measurement may be 32/100 or 32%. Where there aremore or less hash values, the total number of matching hashes may bedivided by the total number of hashes in the hash signature to determinea percentage of similarity for the sampled minimum hash values betweenthe two hash signatures.

At step 1108, the website classification module classifies the websiteand, in some embodiments, the web server based on the similaritymeasurement by comparing the similarity measurement to a similaritythreshold for the classification. The similarity threshold for aparticular classification may be determined based on the type ofclassification and may be different for different types ofclassifications. Thus, the system may determine a similarity thresholdfor the classification, compare the similarity to the similaritythreshold, and may determine whether the website has a classificationbased on whether the similarity measurement exceeds the similaritythreshold. Accordingly, the system may determine the similarity betweenwebsites, determine whether the target website has a similaritymeasurement close enough to meet a similarity threshold corresponding tothe classification and may classify the hash signature and the websiteas having a particular classification based on the similarity threshold.

C. Searching a Corpus of Hash Signatures in Order to Classify a WebsiteUsing a Weighted Similarity Measurement of the Most Similar HashSignatures

Additionally, in some embodiments, a generated hash signature may becompared to a hash signature database including previously generated andclassified hash signatures to determine the similarity of a website tothe previously classified websites using a variety of the closestweighted results in the corpus to determine a classification of a hashsignature.

Thus, some embodiments may be configured to search one or moreclassifications for the closest N number of closest hash signaturesstored in the classified hash signature database. In such embodiments,the system can weigh the similarity of the most similar hash signaturesto determine the distance weighted k-nearest neighbors to ensure thereis not over-fitting based on a particular result or outlier hashsignature. The system can query a database of hash signatures within aclassification and can classify a target website according to theclosest hash signatures within the classification.

Accordingly, in some embodiments, a similarity may be determined thatincludes a weighted similarity of the most similar hash signaturesstored within the classified hash signature database. The closestmatching hash signatures may be classified as confirmed or dismissed forany particular classification. Thus, the system may calculate asimilarity measurement based on the weighted similarity weights based onhash signatures that are the closest matching confirmed hash signaturesfor a classification. Accordingly, in some embodiments, the weightedsimilarity measurement may not provide a true similarity between thenumber of matching vs. total number of hashes. Instead, it may provide aweighted similarity measurement based on the closest confirmed hashsignatures for a particular classification.

For example, if a search within a classification returns ten similarhash signatures, the weighted similarity may include the distancerelated between the top ten results so that there is a linear weightbetween the ten results. This allows the system to provide more value tothe highest weighted results while correcting for false positives. Thus,the eleventh most similar result would be provided a zero weight andthen all the other results may be provided a linear weight up to themost similar which then has a weight of the estimated Jaccardsimilarity. The other results are provided a degrading linear weightbased on the distance of the other results from the most similar result.

A sum of the similarity weights for those ten pages provides a totalweighting for the returned number of similar pages. For instance, ifthere is only one page that is very similar, that hash signature may beweighted very high but that high weighting may be diminished by theother nine hash signatures that are very dissimilar and those will beweighted very low. So when the weightings are summed together, anddivided by the total weighting of all of the returned similarity of thehash signatures, the single confirmed match may be determined to be afalse positive or an outlier.

Accordingly, if there is one really similar result but that result isvery different from the other returned results, the computing device maynot end up being classified with the classification of the single resultdepending on the distribution and weighting of the other hashsignatures. So if the top result is a confirmed page but the otherresults are dismissed pages, these dismissed pages may weight much lowerthan the confirmed page. Only the confirmed weights are summed but thatamount is divided by the weights of all the closest returned results.Thus, if there is a noisy sample where several dismissed matching hashsignatures are very close to the weighting of the single confirmed hashsignature, the similarity score of the returned results may in factbecome low even though a high similarity may be returned for one result.

As such, the dismissed results are included in the returned samples sothat the system can separate dismissed results from the confirmedresults and provide a counter-weight to false-positives. Thus, by onlysumming confirmed results, the system can distinguish between confirmedand dismissed results and determine whether it is more likely that theresult is confirmed or dismissed. Accordingly, some embodiments mayinclude a number of closest matching results (which may includedismissed results) to get a larger sampling size of thee resultsreturned by the search of the closest matching hash signatures to ensurean accurate result. Accordingly, dismissed hash signatures may beincluded in the classification search of a corpus to distinguish betweenconfirmed matches and dismissed matches. Thus, the corpus should beselected to have a non-biased sample. Thus, a corpus of classified hashsignatures may have a mix of confirmed and dismissed hash signatures.

Additionally, by multiplying by the maximum weighting result, the systemcan identify and factor in that none of the results provide a good matchand thus, the system should dismiss the webpage as not being part of theclassification. For example, if the closest match is only 10% similar,the highest score you can have is a 10% similarity measurement. Thus,the website will be classified as dismissed even if all of the resultsare confirmed where they are all only 10% similar (assuming thesimilarity threshold is higher than 10%).

FIG. 12 shows an exemplary method of determining the most similarwebsites to a target website based on the hash signatures of a renderedDOM object of the present/target website and between the stored DOMobject hash signatures of the known websites within one or moreparticular classifications. Before the process shown in FIG. 12 may beperformed the classified hash signature database may be built withconfirmed and dismissed web pages in one or more differentclassifications. The various hash signatures may be classified such thatan elastic search can be performed in a classification to find theconfirmed and dismissed hash signatures with the classification.

At step 1201, a DOM rendering module may render a DOM object of a targetwebsite using the website information associated with the website. Thisprocess is similar to those described above in reference to FIGS. 3 and11.

At step 1202, a hash signature generation module may generate a DOMobject hash value of the target website based on the rendered DOMobject. The DOM object hash value may include a static value that isgenerated by applying a known hash algorithm to the rendered DOM objectof the target website. The same hashing algorithm may have been appliedto the rendered DOM objects of the known websites stored in theclassification database. Thus, static values for the DOM object of thetarget website may be compared directly with the static hash of thepreviously classified websites stored in the classified hash signaturedatabase.

For example, the system may have a classified hash signature databasecontaining thousands of hash signatures generated from the DOM objectsof previously discovered and analyzed websites. Along with each hashsignature, the classified hash signature database may include a statichash (MD5) of the DOM object and labels assigned to each hash signature(e.g. confirmed phishing, confirmed fake pharma, dismissed phishing,etc.). When a candidate page enters the system, a static hash (e.g.,.MD5 hash) may be generated and may be compared to other websites withinthe database. If that static hash (e.g., MD5 hash) exists, the systemdetermines that an exact match has been identified and may assign thesame classification to the target webpage as the website associated withthe matching static hash.

At step 1203, a hash signature generation module may generate a hashsignature of the rendered DOM object of the target website. The hashsignature may be generated using the processes described above inreference to FIGS. 6-11.

At step 1204, a website classification module may identify aclassification for a similarity search of known websites. Theclassification may be determined through any suitable method. Forexample, the classification may entered by an operator, automaticallybased on a manner in which the target website was identified and/orreferred to the computing device, and/or through any other suitablemethod. For example, a target website may be referred to the computingdevice for analysis through a client sending a notification of apotential phishing website address. In such a case, the classificationmay be selected as phishing and the system may search the phishingclassification for classified hash signatures within the storedclassification.

At step 1205, the website classification module may search theclassified hash signature database for websites and corresponding hashsignatures associated with the classification. For example, if theidentified classification is a phishing classification, the computingdatabase may search through the classified phishing classification forhash signatures associated with confirmed and dismissed hash signaturesbeing stored as being within the phishing classification.

At step 1206, the website classification module may compare the hashingsignature of the target website with the stored hash signatures returnedfrom the search through the classified database associated with thedatabase. The computing device may compare each hash value within thetarget hashing signature to the corresponding hash value within theclassified hash signatures stored for that classification. For example,the system may determine the number of classified hash signatures thathave a −13 as the minimum hash value for the first hash function. Thesystem may store the result of the classified website results with amatching result for the first hash function and may continue througheach of the hash values in the target website hash signature.

At step 1207, the website classification module may determine whetherany of the classified hash signatures have an exact match with thetarget website hash signature. Further, before determining a similaritymeasurement between the hash signature, a comparison of the static hashbetween the target website and the classified websites may bedetermined. In some embodiments, this analysis may be done before therest of the hash signature is compared to determine whether any exactcopies of the target website are already included in the corpus to avoidunnecessary processing.

At step 1208, if an exact match between the static hash of the targetwebsite and the classified websites exist, the website classificationmodule may classify the target website as having the sameclassifications as the classified website and the process may eithercontinue or may be stopped. Further, in some embodiments, where thestatic hash between any of the classified websites and the targetwebsite are not found but the sampled hash signature results in an exactmatch, the same classification may be completed as the websites may beso similar that the sampled results were the same. Thus, theclassification is likely accurate as the websites are likely verysimilar. However, the likelihood of similarity may be dependent on theparameters fed into the hashing calculation including the number of dataportions, length of the shingles used, number of hashing functionsapplied to the data portions, etc.

At step 1209, if an exact match is not present, the websiteclassification module may calculate a similarity measurement for each ofthe known websites associated with the classification. The similaritymeasurement may include comparing the target hash signature placementfor each of the hash values within the hash signature and comparing tothe same location within the known hash values of the returned knownhash signatures. Accordingly, the returned hash signatures may besearched for one or more of the most similar hash signatures based onthe similarity measurement for each of the returned known hashsignatures.

At step 1210, the website classification module may identify apredetermined number of the most similar known websites stored withinthe classified hash signature database. The predetermined number of themost similar known websites may include any suitable number. Forexample, 5, 10, 100, and/or any other suitable number of closestmatching hash signatures may be selected. In some embodiments, thenumber of returned closest matches may depend on the number of thestored and classified hash signatures for the classification. Forinstance, where thousands or hundreds of thousands of hash signaturesare stored, it may be beneficial to include a higher number of results,and vice versa.

Accordingly, the computing device may query the data store for the Nclosest hash signatures which may be calculated, selected, and returned.When returned by the database, the similar pages also include asimilarity score. The similarity score may be determined by the numberof hashes in the target hash signature that match the stored hashsignature for each of the known hash signatures. Thus, the N number ofhash signatures that include the most matching hash values as the targethash signature may be returned.

At step 1211, the website classification module may calculate similarityweights of the predetermined number of most similar hash signaturesbased on the similarity distribution of the predetermined number of mostsimilar known website hash signatures. For example, the similarityweights for each of the N closest returned hash signatures may becalculated according to a linear regression from the highest matchedmost similar hash signature.

At step 1212, the website classification module may determine theclassification similarity based on the similarity weights of the mostsimilar known website hash signatures. Further, the similaritymeasurement may be determined only by the weighting of the hashsignatures that are confirmed for the classification. Accordingly, thenumerator for determining the similarity may be the sum of thesimilarity weighting for the websites that are confirmed as being a partof the classification and may not include the similarity weighting ofdismissed hash signatures for the classification. Accordingly, thesimilarity of the target hash signature to the N most similarly returnedhash signatures may include the weighting of the confirmed hashsignatures divided by the sum of all of the weights of the most similarhash signatures multiplied by the most similar hash signature similarityvalue. Thus, some embodiments may implement methods to ensure that falsepositives and/or outlier results to do not overly outweigh the majorityof the returned similarity results.

For instance, FIG. 13 shows an exemplary similarity graph that has adigressing linear weighting based on the distance of the five closestreturned most similar hash signatures.

The x-axis of the similarity distribution shown in FIG. 13 includes adistance calculation for the 5 most similar hash signatures. The y-axisis the weighted similarity for the 5 most similar hash signatures. Thehash signatures 1331-1336 show the 5 most similar hash signatures and alinear digression of the weighted similarity based on the similarity ofeach of the 5 most similar hash signatures. As shown in FIG. 13, themost similar 1331 and the second least similar results 1334 areconfirmed results (indicated by the c designation) and the hashsignatures 1332-1333, 1335 are dismissed for the classification. Thus,the weighted similarity of the 5 most similar results includes the sumof the weighted similarity of the confirmed hash signatures (e.g.,0.85+0.25) divided by the total weighting of all of the most similarresults (e.g., 0.85+0.7+0.5+0.25+0.1) multiplied by the maximumsimilarity (0.85). In this example, the final similarity may be equal to1.1./2.4*0.85=0.39.

At step 1213, the website classification module may classify the targetwebsite as confirmed or dismissed for the classification by comparingthe classification similarity threshold to the similarity weight for thereturned most similar known website hash signatures. Accordingly, theweighted similarity measurement may be calculated and the weightedsimilarity may be compared to a threshold similarity value to determinewhether the hash signature is confirmed or dismissed for theclassification. In the example shown in FIG. 13, the website may beconfirmed or dismissed for the classification based on whether thesimilarity threshold is above or below 0.39. For example, if thesimilarity threshold is 0.4, the website would be dismissed. However, ifthe similarity threshold is 0.35, the website would be confirmed asbeing part of the classification. The classification similarityalgorithm 1340 is shown in FIG. 13.

At step 1214, if another classification is included in a similaritysearch, the process of steps 1204-1213 described above may be repeateduntil there are no more classification searches to be completed. Thus,the DOM signature hash signature can be reused to classify the status ofother page types (e.g. defacement, offensive material, fakepharmaceuticals, etc.). Additionally, in some embodiments, when the pagetype is not specified for the classification, the hash signature may besearched for multiple classifications to determine if the websitematches with different classifications other than the firstclassification in order classify the type of page. Thus, the entireclassification can be searched in some embodiments, to determine theclosest classification or multiple classifications in which the websitecan be confirmed as matching.

At step 1215, if there are no more classification searches to becompleted, a reporting module may report the one or more classificationsassociated with the target website. Additional details are providedbelow for the reporting functionality.

Note that in some embodiments, the embodiments and techniques describedin related U.S. Non-provisional application Ser. No. 14/938,802, titled“IDENTIFYING PHISHING WEBSITES USING DOM CHARACTERISTICS,” filed on Nov.11, 2015, and which is a non-provisional application of and claims thebenefit of priority to U.S. Provisional Application No. 62/219,623,filed Sep. 16, 2015, both of which are hereby incorproated by referencein their entirety for all purposes, may be applied to embodimentsdescribed herein in order to identify website similarity. For example, aphishing model may be generated and used with the hash signatures of thepresent invention to identify similarity between two websites and toidentify websites performing phishing and/or other similarcharacteristics between websites.

III. Action Based on Data Analysis—Mediation

Once the similarity and classification analysis has been completed and awebsite has been classified as similar to a designated classification, areporting module may be configured to take any relevant steps to mediatea website that has been identified as being classified with anundesirable activity.

The computing device may perform any number of different activities tomediate a website identified as being associated with a type ofclassification. For example, the computing device may report theclassification to an entity hosting the web server that is hosting thewebsite. The reporting may be completed through any suitable methodincluding email, text message, phone call, and/or any other suitablemethod for notifying a host and/or control system of the existence of amatching classification. The reporting message may include the websiteinformation, the type of classification, the similarity measurement,and/or any other suitable information that may be helpful in mediatingthe website.

Additionally, the computing device may store the results of thesimilarity analysis and periodically analyze the identified website todetermine whether the website being associated with the classificationhas been “resolved” or taken down. Such a determination may be madewhere a previously classified website that is performing some activityassociated with a classification is identified as no longer performingthat activity such that the website is no longer being confirmed withthat classification (i.e., the web host removed the phishingfunctionality from the web site) and/or the website has been taken down(i.e., the web host killed the website such that it no longer isaccessible). Thus, embodiments allow the computer system to make surethat a classified website is no longer associated with theclassification activity and/or that the website has been taken down. Forexample, a web host may only take down part of a website—not an entiresite. Thus, the system continues to analyze the website periodicallyuntil the website is no longer offending and/or being associated withthe classification activity.

As such, embodiments may classify a website associated with the remoteserver computer as performing or being associated with one or moreclassifications and may report the classified website for one or moremediation activities including take-down, monitoring, and/or are-classification/success reporting action. The system may furtherdetermine an operating status of the website associated with remoteserver computer and report the operating status of the website to amonitoring system to determine whether additional monitoring isnecessary or if the website has been taken down or the classifiedfunctionality has been removed.

IV. Exemplary Computer Sysytem

FIG. 14 shows a block diagram of an example computer system 1400 usablewith system and methods according to embodiments of the presentinvention.

Any of the computer systems mentioned herein may utilize any suitablenumber of subsystems. Examples of such subsystems are shown in FIG. 14in computer apparatus 1400. In some embodiments, a computer systemincludes a single computer apparatus, where the subsystems can be thecomponents of the computer apparatus. In other embodiments, a computersystem can include multiple computer apparatuses, each being asubsystem, with internal components.

The subsystems shown in FIG. 14 are interconnected via a system bus1475. Additional subsystems such as a printer 1474, keyboard 1478, fixeddisk 1479, monitor 1479, which is coupled to display adapter 1482, andothers are shown. Peripherals and input/output (I/O) devices, whichcouple to I/O controller 1471, can be connected to the computer systemby any number of means known in the art, such as serial port 1477. Forexample, serial port 1477 or external interface 1481 (e.g. Ethernet,Wi-Fi, etc.) can be used to connect computer system 1400 to a wide areanetwork such as the Internet, a mouse input device, or a scanner. Theinterconnection via system bus 1475 allows the central processor 1473 tocommunicate with each subsystem and to control the execution ofinstructions from system memory 1472 or the fixed disk 1479, as well asthe exchange of information between subsystems. The system memory 1472and/or the fixed disk 1479 may embody a computer readable medium. Any ofthe values mentioned herein can be output from one component to anothercomponent and can be output to the user.

A computer system can include a plurality of the same components orsubsystems, e.g., connected together by external interface 1481 or by aninternal interface. In some embodiments, computer systems, subsystem, orapparatuses can communicate over a network. In such instances, onecomputer can be considered a client and another computer a server, whereeach can be part of a same computer system. A client and a server caneach include multiple systems, subsystems, or components.

It should be understood that any of the embodiments of the presentinvention can be implemented in the form of control logic using hardware(e.g. an application specific integrated circuit or field programmablegate array) and/or using computer software with a generally programmableprocessor in a modular or integrated manner. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willknow and appreciate other ways and/or methods to implement embodimentsof the present invention using hardware and a combination of hardwareand software.

Any of the software components or functions described in thisapplication may be implemented as software code to be executed by aprocessor using any suitable computer language such as, for example,Java, C++ or Perl using, for example, conventional or object-orientedtechniques. The software code may be stored as a series of instructionsor commands on a computer readable medium for storage and/ortransmission, suitable media include random access memory (RAM), a readonly memory (ROM), a magnetic medium such as a hard-drive or a floppydisk, or an optical medium such as a compact disk (CD) or DVD (digitalversatile disk), flash memory, and the like. The computer readablemedium may be any combination of such storage or transmission devices.

Such programs may also be encoded and transmitted using carrier signalsadapted for transmission via wired, optical, and/or wireless networksconforming to a variety of protocols, including the Internet. As such, acomputer readable medium according to an embodiment of the presentinvention may be created using a data signal encoded with such programs.Computer readable media encoded with the program code may be packagedwith a compatible device or provided separately from other devices(e.g., via Internet download). Any such computer readable medium mayreside on or within a single computer program product (e.g. a harddrive, a CD, or an entire computer system), and may be present on orwithin different computer program products within a system or network. Acomputer system may include a monitor, printer, or other suitabledisplay for providing any of the results mentioned herein to a user.

Any of the methods described herein may be totally or partiallyperformed with a computer system including one or more processors, whichcan be configured to perform the steps. Thus, embodiments can bedirected to computer systems configured to perform the steps of any ofthe methods described herein, potentially with different componentsperforming a respective steps or a respective group of steps. Althoughpresented as numbered steps, steps of methods herein can be performed ata same time or in a different order. Additionally, portions of thesesteps may be used with portions of other steps from other methods. Also,all or portions of a step may be optional. Additionally, any of thesteps of any of the methods can be performed with modules, circuits, orother means for performing these steps.

The specific details of particular embodiments may be combined in anysuitable manner without departing from the spirit and scope ofembodiments of the invention. However, other embodiments of theinvention may be directed to specific embodiments relating to eachindividual aspect, or specific combinations of these individual aspects.

The above description of exemplary embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdescribed, and many modifications and variations are possible in lightof the teaching above. The embodiments were chosen and described inorder to best explain the principles of the invention and its practicalapplications to thereby enable others skilled in the art to best utilizethe invention in various embodiments and with various modifications asare suited to the particular use contemplated.

A recitation of “a”, “an” or “the” is intended to mean “one or more”unless specifically indicated to the contrary.

All patents, patent applications, publications, and descriptionsmentioned above are herein incorporated by reference in their entiretyfor all purposes. None is admitted to be prior art.

1-20. (canceled)
 21. A method comprising, at a computer system:rendering a document object model (DOM) object of a website usingwebsite information; generating, by a hardware processor of the computersystem, a hash signature of the DOM object of the website by: applying anumber of hashing functions to each of a plurality of data portionsseparated from content within the DOM object of the website; andgenerating a hash signature of the DOM object of the website byselecting a number of hashed data portions of the plurality of hasheddata portions; obtaining a known hash signature of a DOM objectassociated with each of a plurality of known websites associated with aclassification; comparing the hash signature of the DOM object of thewebsite to a known hash signature of the DOM object associated with eachof the plurality of known websites, wherein comparing the hash signatureof the DOM object of the website to the known hash signature of the DOMobject associated with each of the plurality of known websites includescomparing each of the plurality of hashed data portions to a pluralityof known hashed data portions of the known hash signature; based ondetermining that the hash signature of the DOM object of the websitedoes not match the known hash signature of the DOM object associatedwith each of the plurality of known websites: for each of the pluralityof known websites associated with the classification: computing asimilarity measurement between the hash signature of the DOM object ofthe website and the known hash signature of the DOM object associatedwith the known website websites associated with the classification;determining whether a threshold associated with the classification issatisfied based on the similarity measurement computed between the hashsignature of the DOM object of the website and the known hash signatureof the DOM object for each of the plurality of known websites; anddetermining whether to classify the website as confirmed for theclassification based on determining whether the threshold associatedwith the classification is satisfied.
 22. The method of claim 21,wherein the website is classified as confirmed for the classificationbased on determining that the threshold associated with theclassification is satisfied, and wherein the website is classified asdismissed for the classification based on determining that the thresholdassociated with the classification is not satisfied.
 23. The method ofclaim 21, further comprising: storing the hash signature of the DOMobject for the website in association with the classification based ondetermining that the website is classified as confirmed for theclassification.
 24. The method of claim 21, wherein the similaritymeasurement includes a magnitude of each of the plurality of hashed dataportions that are shared between the hash signature of the DOM object ofthe website and the known hash signature of the DOM object associatedwith the known website divided by a magnitude of the plurality of hasheddata portions that are shared.
 25. The method of claim 21, furthercomprising: determining a predetermined number of most similar knownhash signatures based on the similarity measurement computed between thehash signature of the DOM object of the website and the known hashsignature of the DOM object associated with each of the plurality ofknown websites; and computing a similarity weight for the predeterminednumber of the most similar known hash signatures; and whereindetermining whether the threshold associated with the classification issatisfied based on the similarity measurement computed between the hashsignature of the DOM object of the website and the known hash signatureof the DOM object associated with each of the plurality of known websites includes determining whether the similarity weight satisfies thethreshold associated with the classification.
 26. The method of claim25, wherein computing a similarity weight for the predetermined numberof the most similar known hash signatures includes: weighting asimilarity of the predetermined number of the most similar known hashsignatures according to a distribution of similarity measurements of thepredetermined number of the most similar known hash signatures; andcalculating the similarity weight for the most similar known hashsignatures by dividing a sum of similarity weights of a number of themost similar known hash signatures that are classified as confirmed by asum of similarity weights of a total number of the most similar knownhash signatures.
 27. The method of claim 21, wherein the number ofhashing functions applied to each of a plurality of data portions is apredetermined number of hashing functions that are generated using acommon seed value, and wherein applying the number of hashing functionsresults in a predetermined number of values for each of the plurality ofdata portions.
 28. The method of claim 21, wherein each of the pluralityof data portions separated from the content within the DOM object of thewebsite have a fixed length.
 29. A system comprising: one or morehardware processors; and a memory device accessible to the one or morehardware processors, wherein the memory device stores one or moreinstructions that, upon execution by the one or more hardwareprocessors, causes the one or more hardware processors to: render adocument object model (DOM) object of a website using websiteinformation; generate a hash signature of the DOM object of the websiteby: applying a number of hashing functions to each of a plurality ofdata portions separated from content within the DOM object; andgenerating a hash signature of the DOM object of the website byselecting a number of hashed data portions of the plurality of hasheddata portions; obtain a known hash signature of the a DOM objectassociated with each of a plurality of known websites associated with aclassification; compare the hash signature of the DOM object of thewebsite to a known hash signature of the DOM object associated with eachof the plurality of known websites, wherein comparing the hash signatureof the DOM object of the website to the known hash signature of the DOMobject associated with each of the plurality of known web sites includescomparing each of the plurality of hashed data portions to a pluralityof known hashed data portions of the known hash signature; based ondetermining that the hash signature of the DOM object of the websitedoes not match the known hash signature of the DOM object associatedwith each of the plurality of known websites: for each of the pluralityof known websites associated with the classification: compute asimilarity measurement between the hash signature of the DOM object ofthe website and the known hash signature of the DOM object associatedwith the known websites associated with the classification; determinewhether a threshold associated with the classification is satisfiedbased on the similarity measurement computed between the hash signatureof the DOM object of the web site and the known hash signature of theDOM object for each of the plurality of known websites; and determinewhether to classify the website as confirmed for the classificationbased on determining whether the threshold associated with theclassification is satisfied.
 30. The system of claim 29, wherein thewebsite is classified as confirmed for the classification based ondetermining that the threshold associated with the classification issatisfied, and wherein the website is classified as dismissed for theclassification based on determining that the threshold associated withthe classification is not satisfied.
 31. The system of claim 29, whereinthe similarity measurement includes a magnitude of each of the pluralityof hashed data portions that are shared between the hash signature ofthe DOM object of the website and the known hash signature of the DOMobject associated with the known website divided by a magnitude of theplurality of hashed data portions that are shared.
 32. The system ofclaim 29, wherein the one or more instructions, upon execution by theone or more hardware processors, further causes the one or more hardwareprocessors to: determine a predetermined number of most similar knownhash signatures based on the similarity measurement computed between thehash signature of the DOM object of the website and the known hashsignature of the DOM object associated with each of the plurality ofknown websites; and compute a similarity weight for the predeterminednumber of the most similar known hash signatures; and whereindetermining whether & the threshold associated with the classificationis satisfied based on the similarity measurement computed between thehash signature of the DOM object of the website and the known hashsignature of the DOM object associated with each of the plurality ofknown websites includes determining whether the similarity weightsatisfies the threshold associated with the classification.
 33. Thesystem of claim 32, wherein computing a similarity weight for thepredetermined number of the most similar known hash signatures includes:weighting a similarity of the predetermined number of the most similarknown hash signatures according to a distribution of similaritymeasurements of the predetermined number of the most similar known hashsignatures; and calculating the similarity weight for the most similarknown hash signatures by dividing a sum of similarity weights of anumber of the most similar known hash signatures that are classified asconfirmed by a sum of similarity weights of a total number of the mostsimilar known hash signatures.
 34. The system of claim 29, wherein thenumber of hashing functions applied to each of a plurality of dataportions is a predetermined number of hashing functions that aregenerated using a common seed value, and wherein applying the number ofhashing functions results in a predetermined number of values for eachof the plurality of data portions.
 35. The system of claim 29, whereinthe classification is a first classification as a phishing website, asecond classification as copyrighted content, or a third classificationas unauthorized content.
 36. A non-transitory computer-readable storagemedium storing one or more instructions that, upon execution by one ormore processors, causes the one or more processors to: render a documentobject model (DOM) object of a website using website information;generate a hash signature of the DOM object of the website by: applyinga number of hashing functions to each of a plurality of data portionsseparated from content within the DOM object of the website; andgenerating a hash signature of the DOM object of the website byselecting a number of hashed data portions of the plurality of hasheddata portions; obtain a known hash signature of a DOM object associatedwith each of a plurality of known websites associated with aclassification; compare the hash signature of the DOM object of thewebsite to a known hash signature of the DOM object associated with eachof the plurality of known websites, wherein comparing the hash signatureof the DOM object of the website to the known hash signature of the DOMobject associated with each of the plurality of known websites includescomparing each of the plurality of hashed data portions to a pluralityof known hashed data portions of the known hash signature; based ondetermining that the hash signature of the DOM object of the websitedoes not match the known hash signature of the DOM object associatedwith each of the plurality of known websites: for each of the pluralityof known websites associated with the classification: compute asimilarity measurement between the hash signature of the DOM object ofthe website and the known hash signature of the DOM object associatedwith the known websites associated with the classification; determinewhether a threshold associated with the classification is satisfiedbased on the similarity measurement computed between the hash signatureof the DOM object of the website and the known hash signature of the DOMobject for each of the plurality of known websites; and determinewhether to classify the website as confirmed for the classificationbased on determining whether the threshold associated with theclassification is satisfied.
 37. The non-transitory computer-readablestorage medium of claim 36, wherein the website is classified asconfirmed for the classification based on determining that the thresholdassociated with the classification is satisfied, and wherein the websiteis classified as dismissed for the classification based on determiningthat the threshold associated with the classification is not satisfied.38. The non-transitory computer-readable storage medium of claim 36,wherein the one or more instructions, upon execution by the one or moreprocessors, further causes the one or more processors to: determine apredetermined number of most similar known hash signatures based on thesimilarity measurement computed between the hash signature of the DOMobject of the website and the known hash signature of the DOM objectassociated with each of the plurality of known websites; and compute asimilarity weight for the predetermined number of the most similar knownhash signatures; and wherein determining whether & the thresholdassociated with the classification is satisfied based on the similaritymeasurement computed between the hash signature of the DOM object of thewebsite and the known hash signature of the DOM object associated witheach of the plurality of known web sites includes determining whetherthe similarity weight satisfies the threshold associated with theclassification.
 39. The non-transitory computer-readable storage mediumof claim 36, wherein the number of hashing functions applied to each ofa plurality of data portions is a predetermined number of hashingfunctions that are generated using a common seed value, and whereinapplying the number of hashing functions results in a predeterminednumber of values for each of the plurality of data portions.
 40. Thenon-transitory computer-readable storage medium of claim 36, wherein theclassification is a first classification as a phishing website, a secondclassification as copyrighted content, or a third classification asunauthorized content.